Automated on-site broiler live weight estimation through YOLO-based segmentation
Broiler weighing is essential in poultry production for growth monitoring, feed management, health detection, and meeting market requirements. Traditional weighing methods, which use electronic platform weighers, can stress broilers and may not capture accurate weight data, particularly heavy broile...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525000619 |
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| author | Mahmoud Y. Shams Wael M. Elmessery Awad Ali Tayoush Oraiath Ahmed Elbeltagi Ali Salem Pankaj Kumar Tamer M. El-Messery Tarek Abd El-Hafeez Mohamed F. Abdelshafie Gomaa G. Abd El-Wahhab Ibrahim S. El-Soaly Abdallah Elshawadfy Elwakeel |
| author_facet | Mahmoud Y. Shams Wael M. Elmessery Awad Ali Tayoush Oraiath Ahmed Elbeltagi Ali Salem Pankaj Kumar Tamer M. El-Messery Tarek Abd El-Hafeez Mohamed F. Abdelshafie Gomaa G. Abd El-Wahhab Ibrahim S. El-Soaly Abdallah Elshawadfy Elwakeel |
| author_sort | Mahmoud Y. Shams |
| collection | DOAJ |
| description | Broiler weighing is essential in poultry production for growth monitoring, feed management, health detection, and meeting market requirements. Traditional weighing methods, which use electronic platform weighers, can stress broilers and may not capture accurate weight data, particularly heavy broilers. To overcome these limitations, this study proposes a camera-based weighing approach that relies on morphological changes in different growth stages of broilers rather than body dimensions. The study utilizes YOLO version 8, a deep learning-based network segmentation technique, for precise broiler segmentation, significantly improving weight accuracy in complex environments. The YOLOv8 architecture builds a model that demonstrates improved and trustworthy results in broiler weight prediction, achieving a mean average precision across a range of intersection over union thresholds from 50 % to 95 % of 0.829. By accurately estimating broiler weights based on their morphological features, the developed trained YOLOv8 model eliminates the need for measuring their dimensions or sizes, making the process efficient and convenient. |
| format | Article |
| id | doaj-art-0c3b3fdf1add4187a2e31a758485cb84 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-0c3b3fdf1add4187a2e31a758485cb842025-02-10T04:35:29ZengElsevierSmart Agricultural Technology2772-37552025-03-0110100828Automated on-site broiler live weight estimation through YOLO-based segmentationMahmoud Y. Shams0Wael M. Elmessery1Awad Ali Tayoush Oraiath2Ahmed Elbeltagi3Ali Salem4Pankaj Kumar5Tamer M. El-Messery6Tarek Abd El-Hafeez7Mohamed F. Abdelshafie8Gomaa G. Abd El-Wahhab9Ibrahim S. El-Soaly10Abdallah Elshawadfy Elwakeel11Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheikh University Kafr Elsheikh, 33516, EgyptAgricultural Engineering Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Shaikh, 33516, EgyptDepartment of Agricultural Engineering, Faculty of Agriculture, Omar Al Mukhtar University, Al Bayda PO Box 991, LibyaAgricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, EgyptCivil Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt; Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pécs , Pécs 7622, Hungary; Corresponding author.International Research Centre “Biotechnologies of the Third Millennium”, Faculty of Biotechnologies (BioTech), ITMO University, St. Petersburg, 191002, RussiaInternational Research Centre “Biotechnologies of the Third Millennium”, Faculty of Biotechnologies (BioTech), ITMO University, St. Petersburg, 191002, RussiaDepartment of Computer Science, Faculty of Science, Minia University, Minia, 61519, Egypt; Computer Science Unit, Deraya University, Minia University, Minia, 61765, EgyptDepartment of Agricultural Constructions Engineering and Environmental Control, Faculty of Agricultural Engineering, Al-Azhar University, Cairo, EgyptDepartment of Agricultural Constructions Engineering and Environmental Control, Faculty of Agricultural Engineering, Al-Azhar University, Cairo, EgyptDepartment of Agricultural Constructions Engineering and Environmental Control, Faculty of Agricultural Engineering, Al-Azhar University, Cairo, EgyptAgricultural Engineering Department, Faculty of Agriculture and Natural resources, Aswan University, Aswan, EgyptBroiler weighing is essential in poultry production for growth monitoring, feed management, health detection, and meeting market requirements. Traditional weighing methods, which use electronic platform weighers, can stress broilers and may not capture accurate weight data, particularly heavy broilers. To overcome these limitations, this study proposes a camera-based weighing approach that relies on morphological changes in different growth stages of broilers rather than body dimensions. The study utilizes YOLO version 8, a deep learning-based network segmentation technique, for precise broiler segmentation, significantly improving weight accuracy in complex environments. The YOLOv8 architecture builds a model that demonstrates improved and trustworthy results in broiler weight prediction, achieving a mean average precision across a range of intersection over union thresholds from 50 % to 95 % of 0.829. By accurately estimating broiler weights based on their morphological features, the developed trained YOLOv8 model eliminates the need for measuring their dimensions or sizes, making the process efficient and convenient.http://www.sciencedirect.com/science/article/pii/S2772375525000619Camera-based weighing systemMorphological evolutionsYOLOv8 |
| spellingShingle | Mahmoud Y. Shams Wael M. Elmessery Awad Ali Tayoush Oraiath Ahmed Elbeltagi Ali Salem Pankaj Kumar Tamer M. El-Messery Tarek Abd El-Hafeez Mohamed F. Abdelshafie Gomaa G. Abd El-Wahhab Ibrahim S. El-Soaly Abdallah Elshawadfy Elwakeel Automated on-site broiler live weight estimation through YOLO-based segmentation Smart Agricultural Technology Camera-based weighing system Morphological evolutions YOLOv8 |
| title | Automated on-site broiler live weight estimation through YOLO-based segmentation |
| title_full | Automated on-site broiler live weight estimation through YOLO-based segmentation |
| title_fullStr | Automated on-site broiler live weight estimation through YOLO-based segmentation |
| title_full_unstemmed | Automated on-site broiler live weight estimation through YOLO-based segmentation |
| title_short | Automated on-site broiler live weight estimation through YOLO-based segmentation |
| title_sort | automated on site broiler live weight estimation through yolo based segmentation |
| topic | Camera-based weighing system Morphological evolutions YOLOv8 |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525000619 |
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